Minds after Death: The Expanding Role of Psychological Autopsy in Investigations: A Review
Bibliographic record
Abstract
Deaths can result from deliberate self-harm (DSH), accidents, natural causes, homicides, or remain unidentified, causing prolonged distress for the deceased’s family and challenges for authorities. Suicide, a significant public health concern, exemplifies self-destructive behavior often unnoticed or partially noticed. Psychological Autopsy (PA) is highly needed in India due to the significant suicide rate and the complex factors contributing to it. Various nations, including the USA, UK, Canada and Australia, have already recognized psychological autopsy as crucial evidence in court. Although PAs are performed in India, their legal acceptability remains debated. It helps in giving a lesser clouded vision of the victim profile and at times even facilitates the specific definition of the cause of death. Studies reveal that about 90% of those who commit suicide suffer from one or more mental disorders, with depression most common; hence, this finding has been beneficial in identification and treatment of such cases at earliest so as to prevent suicide. Recommendations for the future development of this method include embracing modern communication methods and ‘invisible informants’, cultural intersections, safeguarding of reliability and validity, and the use of feasibility trials. The emphasis remains on collating the raw narratives at the core of these interviews, which make the psychological autopsy such a unique and perceptive tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".